Alternatives Assessment 123 Webinar: Mechanistic Data in a - - PowerPoint PPT Presentation

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Alternatives Assessment 123 Webinar: Mechanistic Data in a - - PowerPoint PPT Presentation

Alternatives Assessment 123 Webinar: Mechanistic Data in a Systematic Review Framework: Developing Confidence in Bodies of Evidence JANUARY 21, 2015 FACILITATED BY: JOEL TICKNER, SCD JOEL_TICKNER@UML.EDU LOWELL CENTER FOR SUSTAINABLE


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JANUARY 21, 2015 FACILITATED BY: JOEL TICKNER, SCD JOEL_TICKNER@UML.EDU LOWELL CENTER FOR SUSTAINABLE PRODUCTION, UMASS LOWELL

Alternatives Assessment 123 Webinar: Mechanistic Data in a Systematic Review Framework: Developing Confidence in Bodies of Evidence

* If you would like to ask a question or comment during this webinar please type your question in the Q&A box located in the control panel.

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— Continuing education and dialog — To advance the practice of alternatives assessment for

informed substitution across federal, state, and local agencies through networking, sharing of experiences, development of common approaches, tools, datasets and frameworks, and creation of a community of practice.

Goals

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Purpose of this call

  • Alternatives assessment processes often suffer from

significant gaps in toxicological data

  • High throughput in-vitro screens provide a means to fill

data gaps and serve as primary data

  • The objective of the Tox 21 partnership, a multi-agency

collaborative effort, is to shift the assessment of chemical hazards from traditional experimental animal toxicology studies to one based on target-specific, mechanism- based, biological observations largely obtained using in vitro assays, with the ultimate aim of improving risk assessment for humans and the environment and the design of safer chemicals

  • A key goal is to develop better predictive tools
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To view program and to register visit: www.saferalternatives.org

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  • Dr. Andrew Rooney

Office of Health Assessment and Translation, National Institute of Environmental Health Sciences

Speaker

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Discussion Questions

  • What are the strengths and weaknesses of high

throughput screening data for both data gap filling and as primary data to support alternatives assessment?

  • How useful/usable is Tox 21 data for both

comparing chemical alternatives and designing safer molecules at the present time.

  • What are the challenges and opportunities to more

effective integration of these data streams in chemical alternatives assessment?

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— Due to the number of participants on the Webinar, all lines

will be muted.

— If you wish to ask a question, please type your question in

the Q&A box located in the drop down control panel at the top of the screen.

— All questions will be answered at the end of the

presentations.

Webinar Discussion Instructions

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Mechanistic Data in a Systematic Review Framework: Developing Confidence in Bodies of Evidence

Andrew Rooney, PhD Office of Health Assessment and Translation National Institute of Environmental Health Sciences

January 21, 2015

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  • Background

– Office of Health Assessment and Translation – Systematic review, mechanistic data, and environmental health questions

  • Mechanistic Data in the OHAT Framework

– Planning – Identifying the evidence – Evaluating the evidence – Integrating the evidence

  • Challenges and Ongoing Methods Development
  • Questions

– SR on OHAT Website (http://ntp.niehs.nih.gov/go/38673)

Presentation Outline

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  • Conduct literature-based evaluations

to assess the evidence that environmental substances cause adverse health effects

– Hazard or State-of-science evaluations – Provide opinions on whether substances may be of concern given current human exposure

  • Methods development

– Systematic review – Increasing integration of mechanistic data – Approaches to assess confidence in mechanistic data

Office of Health Assessment and Translation

U.S. Department of Health and Human Services National Toxicology Program

NTP NTP Monograph

Developmental Effects and Pregnancy Outcomes Associated With Cancer Chemotherapy Use During Pregnancy Month 2013

OHAT Evaluations

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  • Address breadth of relevant data

– Wide range of human study designs – Animal studies – Mechanistic studies (in vitro and other relevant data)

  • Procedure to integrate evidence streams
  • Dual role for mechanistic data

– 1) Integrate with human/animal evidence – 2) Potential to support decisions in absence of human or animal data

Requirements for Environmental Health

Systematic Review

Human studies Animal studies Mechanistic studies Alternatives often have small, primarily mechanistic data sets

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  • Evidence Integration

– The process for reaching conclusions on the NTP’s confidence across a body of studies within an evidence stream (i.e., human and animal data separately) and then integrating those conclusions across the evidence streams with consideration of other relevant data such as supporting evidence from mechanistic studies – Lack of consensus on term “Weight of Evidence”? (Weed et al., 2005)

Extends Existing Systematic Review Methods

OHAT Framework

Problem Formulation and Prepare Protocol Search for and Select Studies for Inclusion Extract Data from Studies Assess Quality of Individual Studies Rate Confidence in Body of Evidence Translate Confidence into Level of Evidence for Health Effects Integrate Evidence to Develop Hazard ID Conclusions

Systematic Review Evidence Integration

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Integrate Evidence

Increased transparency and objectivity

  • Applied to all three evidence streams (human, animal, mechanistic)
  • Framework for documenting the basis of scientific judgments

− Individual study quality − Confidence in bodies of evidence − Hazard ID conclusions

  • Procedures to integrate evidence streams

Systematic Review and Evidence Integration

OHAT Framework

Human Animal Mechanistic

Systematic Review

Evaluate Evidence

Human Animal Mechanistic Human Animal Mechanistic

Evidence Integration

Develop Protocol Identify Evidence

Search for Studies Extract Data Select Studies Assess Individual Study Quality Develop Hazard Identification Conclusions Rate Confidence in Bodies Of Evidence

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Planning the Evaluation

Integrate Evidence

Human Animal Mechanistic

Systematic Review

Evaluate Evidence

Human Animal Mechanistic

Evidence Integration

Identify Evidence

Search for Studies Extract Data Select Studies Assess Individual Study Quality Develop Hazard Identification Conclusions Rate Confidence in Bodies Of Evidence

Planning

Scoping Problem Formulation Broad Literature Search

Human Animal Mechanistic

Develop Protocol

Planning step develops

  • Objectives
  • Study question
  • PECO statement
  • Population
  • Exposures
  • Comparators
  • Outcomes
  • Protocol
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  • PECOS for Human and non-human animal evidence

– Population: Humans or animals without restriction on sex or life stage – Exposure: PFOA (CAS# 335-67-1) or PFOS (CAS# 1763-23-1) or their salts – Comparator: Humans or animals exposed to lower levels or vehicle – Outcomes:

  • Primary outcomes: Immune-related diseases and measures of immune function
  • Secondary outcomes: Immunostimulation and observational immune endpoints
  • What about Mechanistic evidence?

– Outcomes:

  • Primary outcomes: Measures of immune function after in vitro exposure
  • Secondary outcomes: Observational immune endpoints after in vitro exposure

Example: Evaluation of PFOA/PFOS immunotoxicity

PECO Statement

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  • How broadly should one collect mechanistic evidence?

– For narrow, well-defined outcomes

  • PECO for mechanistic data developed in protocol
  • May require technical experts to ID mechanisms and list of search terms

– For multiple outcomes or general health effects reviews

  • 1) Identify relevant mechanistic data if clearly known
  • 2) Plan to supplement with outcome-relevant mechanistic data

– After health effects are identified, additional search may be warranted – All changes are documented

Mechanistic data should address relevant outcomes

Consider Supplementing PECO Statement

Relevant outcomes may not be clear until after human and animal data are collected

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Identifying the Evidence

Systematic Review

Integrate Evidence

Human Animal Mechanistic

Systematic Review

Evaluate Evidence

Human Animal Mechanistic Human Animal Mechanistic

Evidence Integration

Develop Protocol Identify Evidence

Search for Studies Extract Data Select Studies Assess Individual Study Quality Develop Hazard Identification Conclusions Rate Confidence in Bodies Of Evidence

  • Search for Studies
  • Select Studies
  • Extract Data

Challenge for Mechanistic Data

How narrowly do you define “relevant” studies? Or Which mechanistic data are relevant?

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References identified through database searches (n=5,534) References identified through other sources (n=4)

Identification Screening Included

Selecting Studies: PFOA/PFOS Immunotoxicity

Identify Evidence

References excluded for criteria established in protocol (n=2,364) # of full-text articles excluded for pre-established criteria, with reasons

  • Exposure not relevant (n=38)
  • Outcome not relevant (n=34)
  • Review (n=74)
  • Other (n=55)
  • Not relevant:14
  • Pharmacokinetic data only: 9
  • Meeting Abstract Only: 26
  • Grants: 6

Studies included for data extraction in step 3, and risk of bias assessment in step 4 (n=114) Full-text articles assessed for relevance and eligibility (n= 315) References after duplicate removal Title-abstract screened for relevance and eligibility (n=2,675) Animal studies (n=80) Human studies (n=18) Mechanistic studies (n=19)

n=3

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  • Multiple aspects of “quality” and “utility” are important

– Risk of bias or internal validity How credible are findings based on study design and conduct? – Reporting quality How well was the study reported? – Directness and applicability How well does the study address the topic under review?

Assessing Individual Study Quality

Evaluate Evidence

Integrate Evidence

Human Animal Mechanistic

Systematic Review

Evaluate Evidence

Human Animal Mechanistic Human Animal Mechanistic

Evidence Integration

Develop Protocol Identify Evidence

Search for Studies Extract Data Select Studies Assess Individual Study Quality Develop Hazard Identification Conclusions Rate Confidence in Bodies Of Evidence

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  • Established risk of bias tools for randomized controlled trials
  • Emerging methods on how to assess risk of bias for:

– Observational human studies – Animal studies – Mechanistic studies?

  • OHAT draft approach

Published Approaches and Challenges

Risk of Bias

EBPCs

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  • Predefined set of questions adapted from AHRQ to address

– Human studies – Animal toxicology studies – In vitro/mechanistic studies

  • Study design determines which questions are applicable

– Answers equate to risk of bias rating for each question – Answers on 4-point scale from Clarity Group – Evaluation is endpoint specific

A “Parallel” Approach Across Evidence Streams

OHAT Risk of Bias Tool

++ + − −−

Definitely Low risk of bias Probably Low risk of bias Probably High risk of bias Definitely High risk of bias

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  • 1. Was administered dose or exposure level adequately randomized?
  • 2. Was allocation to study groups adequately concealed?
  • 3. Did selection of study participants result in appropriate comparison groups?
  • 4. Did study design or analysis account for important confounding and modifying variables?
  • 5. Were experimental conditions identical across study groups?
  • 6. Were research personnel and human subjects blinded to study group during the study?
  • 7. Were outcome data complete without attrition or exclusion from analysis?
  • 8. Can we be confident in the exposure characterization?
  • 9. Can we be confident in the outcome assessment?
  • 10. Were all measured outcomes reported?

11: Were there other potential threats to internal validity?

Single set of Questions

OHAT Risk of Bias Questions

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  • 1. Was administered dose or exposure level adequately randomized?
  • 2. Was allocation to study groups adequately concealed?
  • 3. Did selection of study participants result in appropriate comparison groups?
  • 4. Did study design or analysis account for important confounding and modifying variables?
  • 5. Were experimental conditions identical across study groups?
  • 6. Were research personnel and human subjects blinded to study group during the study?
  • 7. Were outcome data complete without attrition or exclusion from analysis?
  • 8. Can we be confident in the exposure characterization?
  • 9. Can we be confident in the outcome assessment?
  • 10. Were all measured outcomes reported?
  • 11. Other potential threats to internal validity?

Questions for Experimental Studies

OHAT Risk of Bias Tool

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Evidence from Study Report or Author Contact

Developing Each Risk of Bias Rating

Specific Guidance

Guidance defines all 4 ratings for each question

  • 1. Randomization
  • Definitely Low risk of bias: There is direct evidence that

animals were allocated to any study group including controls using a method with a random component. Restricted randomization (e.g., blocked) will be considered low bias …

  • Probably Low risk of bias: There is indirect evidence that

animals were allocated to any study group including controls using a method with a random component (i.e., authors state that allocation was random …

  • Probably High risk of bias: ….
  • Definitely High risk of bias: …

Was administered dose or exposure adequately randomized?

Support for final rating: “mice were randomly divided by weight”

  • PFOS. Seventy-two mice were then randomly divided by

weight into six groups of 12/group. Once distributed into groups, the mice were acclimated to cage conditions and

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  • Was administered dose or exposure level adequately

randomized?

– Helps to assure that treatment is not given selectively based on potential differences in human subjects, animals, cells, or tissues – Requires each human subject, animal, or cell had an equal chance

  • f being assigned to any study group including controls

– In vitro /mechanistic applicability

  • Potential differences between cells that comprise different groups will

depend on study design

– Finite cell strains with document number of population doublings – Primary cells from multiple donors – Homogeneous cell suspension

Extending Methods to Mechanistic Studies

Risk of Bias for Experimental Studies

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In vitro-specific Guidance

Risk of Bias for Experimental Studies

  • 1. Was administered dose or exposure level adequately randomized?

Experimental Animal Studies

  • Definitely low risk of bias

– Direct evidence that animals were allocated to any study group including controls using a method with a random component. – Restricted randomization (e.g., blocked randomization) is considered acceptable – Requires concurrent controls

In vitro studies

  • Definitely low risk of bias

– Direct evidence that cells were allocated to any study group including controls using a method with a random component. – OR all cells in culture come from a homogenous cell suspension recently collected from cell culture vessels following appropriate techniques – Requires concurrent control

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Evidence Integration

Integrate Evidence

Human Animal Mechanistic

Systematic Review

Evaluate Evidence

Human Animal Mechanistic Human Animal Mechanistic

Evidence Integration

Develop Protocol Identify Evidence

Search for Studies Extract Data Select Studies Assess Individual Study Quality Develop Hazard Identification Conclusions Rate Confidence in Bodies Of Evidence

  • Rating Confidence in Bodies of Evidence
  • Integrating Evidence Streams for Hazard ID
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  • Developing confidence ratings

– How confident are you that findings from a group of studies reflect the true relationship between exposure to a substance and effect?

  • GRADE Working Group

– Widely accepted method for rating confidence in a body of evidence on healthcare interventions

  • OHAT Framework

– Guidance for human and animal studies – Parallel approach for mechanistic studies – Initial confidence stratified based on study design features

Rating Confidence in Bodies of Evidence

Evidence Integration

Moderate (+++) 3 Features Low (++) 2 Features Very Low (+) 1≤ Features High (++++)

4 Features

Initial Confidence Cohort Case series Case report Ecologic Case-control Cross-sectional Human controlled trial Experimental animal

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Each Body of Evidence

  • outcomes or
  • related outcomes

(as specified in protocol)

Example from Human Observational Studies

Rating Confidence in the Body of Evidence

Factors Decreasing Confidence

  • unexplained inconsistency
  • risk of bias
  • Indirectness/applicability
  • imprecision
  • publication bias

Factors Increasing Confidence

  • magnitude of effect
  • dose response
  • Consistency (species/populations)
  • residual confounding
  • other

Moderate (+++)

3 Features

Low (++)

2 Features

Very Low (+)

1≤ Features

High (++++)

4 Features

Initial Confidence

Cohort 3-features Case-Control 3-features

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  • Rating confidence in bodies of evidence

– Factors increasing/decreasing confidence

  • Parallel factors for mechanistic data

– magnitude of effect ≈ potency – dose-response – consistency – risk of bias – directness/applicability ≈ relevance

  • pathway for human health
  • concentration for human exposure

– publication bias

  • Other developing approaches

– Similarity profiles – Exploring utility of pathway approach (AOP)

Extending Methods to Mechanistic data

Rating Confidence in the Body of Evidence

Factors Decreasing Confidence

  • unexplained inconsistency
  • risk of bias
  • indirectness/applicability
  • publication bias
  • imprecision

Factors Increasing Confidence

  • magnitude of effect
  • dose response
  • consistency (species/population)
  • residual confounding
  • other

Factors Considered for Human and Animal Evidence

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Evidence Integration: Develop Hazard ID

Two part process

– Consider human and animal evidence together – Consider impact of mechanistic data

  • in vitro data, or
  • upstream indicators
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Two part process

Evidence Integration: Develop Hazard ID

– Consider human and animal evidence together – Consider impact of mechanistic data

  • in vitro data, or
  • upstream indicators

– strong support? – strong opposition?

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Level of Evidence for Health Effects in Human Studies Level of Evidence for Health Effects in Animal Studies

Evidence Integration: Develop Hazard ID

Level of Evidence Reflects Confidence in Data

High Moderate Low / High Moderate Low/Inadequate Inadequate

– Low / Inadequate Level of Evidence

  • Low confidence in

body of evidence for an association between exposure and health outcome

  • Or no data available

Alternatives may have no human data

“Presumed”

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Level of Evidence for Health Effects in Human Studies Level of Evidence for Health Effects in Animal Studies

Evidence Integration: Develop Hazard ID

Level of Evidence Reflects Confidence in Data

High Moderate Low / High Moderate Low/Inadequate Inadequate

“Suspected” – Low / Inadequate Level of Evidence

  • Low confidence in

body of evidence for an association between exposure and health outcome

  • Or no data available

Alternatives may have no human data

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Level of Evidence for Health Effects in Human Studies Level of Evidence for Health Effects in Animal Studies

Evidence Integration: Develop Hazard ID

Level of Evidence Reflects Confidence in Data

High Moderate Low / High Moderate Low/Inadequate Inadequate

“Not classifiable”

– Low / Inadequate Level of Evidence

  • Low confidence in

body of evidence for an association between exposure and health outcome

  • Or no data available

Alternatives may have no human data and no animal data

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Level of Evidence for Health Effects in Human Studies Level of Evidence for Health Effects in Animal Studies

Evidence Integration: Develop Hazard ID

Challenging decisions in absence of human or animal data

High Moderate Low / High Moderate Low/Inadequate Inadequate

“Not classifiable”

– Mechanistic data

  • in vitro data, or
  • upstream indicators

– It is envisioned that strong evidence for a relevant biological process from mechanistic data could result in a conclusion of “suspected” in the absence of human or animal data

“Suspected”

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Challenges for Mechanistic Data

Rate Confidence in Body of Evidence Assess Individual Study Quality Search for and Select Studies, Extract Data Rate Confidence in Body of Evidence Assess Individual Study Quality Search for and Select Studies, Extract Data Search for and Select Studies, Extract Data ??? ??? Assess Individual Study Quality

Draft OHAT risk-of-bias tools for in vitro studies No established framework for rating confidence in bodies of evidence for diverse mechanistic data Established methods

Human Studies Animal Studies Mechanistic Data

Identify Evidence Evaluate Evidence Integrate Evidence OHAT Considers Parallel Factors

Rate Confidence in Body of Evidence

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  • Systematic review procedures are being used to address questions in

toxicology and environmental health

  • The OHAT Framework uses a parallel approach for all three evidence

streams (human, animal, mechanistic studies)

  • Alternatives are likely to support lower confidence but still potential to

support decision making

  • Focus for methods development and refinement

– Risk of bias tool for in vitro/mechanistic studies – Developing confidence ratings in mechanistic studies for integrating with human/animal effects – Developing confidence ratings in mechanistic studies for use as stand-alone evidence

Systematic Review and Mechanistic Data

Summary ü ü

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Acknowledgements

  • Office of Health Assessment and Translation

– Abee Boyles – Kembra Howdeshell – Katie Pelch – Kyla Taylor – Kristina Thayer, Director – Vickie Walker

  • Technical Advisors and Experts Consulted in

Development of OHAT Framework

– Lisa Bero, Director, San Francisco Branch, United States Cochrane Center at UC San Francisco – Gordon Guyatt, Co-chair, GRADE Working Group, McMaster University – Malcolm Macleod, CAMARADES Centre, University of Edinburgh – Karen Robinson, Co-Director, Evidence-Based Practice Center, Johns Hopkins Bloomberg School of Public Health – Holger Schünemann, Co-chair, GRADE Working Group, McMaster University – Tracey Woodruff, Director, Program on Reproductive Health and the Environment, UCSF

  • Technical Advisors on PFOA/PFOS Protocol

– Jamie Dewitt, ECU Department Pharmacology and Toxicology – Tony Fletcher, London School of Hygiene and Tropical Med. – Dori Germolec, NIEHS/NTP – Christopher Lau, US EPA, ORD/NHEERL – Roberta Scherer, Johns Hopkins, Bloomberg School Pub.H.

  • Office of Science Information

Management

– Stephanie Holmgren

  • National Toxicology Program

– John Bucher, Associate Director – Mary Wolfe, Director OLPR – Lori White (OLPR)

  • NTP BSC Working Group
  • Navigation Guide Work Group
  • Interagency Comments

– ATSDR – EPA – DoD – FDA – NIOSH

  • Public Comments
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Thank You

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Discussion Questions

  • What are the strengths and weaknesses of high

throughput screening data for both data gap filling and as primary data to support alternatives assessment?

  • How useful/usable is Tox 21 data for both

comparing chemical alternatives and designing safer molecules at the present time.

  • What are the challenges and opportunities to more

effective integration of these data streams in chemical alternatives assessment?

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Alternatives Assessment 124: The Use of QSARs, Read Across, and Analogue Approaches to Inform Decision Making in Alternatives Assessments February 17, 2015, 12pm est

Presenters: Dr. Jay Tunkel and Ms. Cathy Rudisill, SRC, Inc.

Alternatives Assessments under REACH: Lessons Learned

  • Feb. 26, 2015, 11am est

Presenters: Thierry Nicot and Denis Mottet, European Chemicals Agency Tatiana Santos, European Environment Bureau Julius Waller EPPA Inc.

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